December 16, 2019

139 words 1 min read

Hacking web performance

Hacking web performance

After you understand how important web performance is and have applied basic techniques, what's next? Max Firtman covers extreme web performance techniques that will blow your mind, from new compression algorithms and new image formats to client hints and HTTP/2 push. Join in to learn how to hack web performance.
Talk Title Hacking web performance
Speakers Max Firtman (ITMaster Professional Training)
Conference O’Reilly Fluent Conference
Conf Tag The Web Platform in Practice
Location San Jose, California
Date June 12-14, 2018
URL Talk Page
Slides Talk Slides
Video

After you understand how important web performance is and have applied basic techniques, what’s next? Max Firtman covers extreme web performance techniques that will blow your mind, from new compression algorithms and new image formats to client hints and HTTP/2 push. Join in to learn how to hack web performance. Topics include:

algorithm performance http/2 hacking
comments powered by Disqus
To push, or not to push? The future of HTTP/2 server push

To push, or not to push? The future of HTTP/2 server push

December 13, 2019

HTTP/2 server push gives us the ability to proactively send assets to a browser without waiting for them to be requested. Sounds great, but is this new mechanism really a silver bullet? Using new research and real-world examples, Patrick Hamann leads a deep dive into server push and attempts to answer the question we're all asking: Is it ready for production?
Practical considerations when shifting to using deep learning for your text data

Practical considerations when shifting to using deep learning for your text data

December 3, 2019

Emmanuel Ameisen and Yan Kou share a guide for moving your company toward deep learning using a collection of NLP best practices gathered from conversations with 75+ teams from Google, Facebook, Amazon, Twitter, Salesforce, Airbnb, Capital One, Bloomberg, and others.
Deep learning for domain-specific entity extraction from unstructured text

Deep learning for domain-specific entity extraction from unstructured text

November 27, 2019

Mohamed AbdelHady and Zoran Dzunic demonstrate how to build a domain-specific entity extraction system from unstructured text using deep learning. In the model, domain-specific word embedding vectors are trained on a Spark cluster using millions of PubMed abstracts and then used as features to train a LSTM recurrent neural network for entity extraction.
Envoy Internals Deep Dive

Envoy Internals Deep Dive

November 25, 2019

Envoy (https://www.envoyproxy.io/) is a high performance L4/L7 network proxy with a pluggable filter chain model; this allows Envoy to be used for a variety of different scenarios, including HTTP/2, g …
Human in the loop: Bayesian rules enabling explainable AI

Human in the loop: Bayesian rules enabling explainable AI

November 25, 2019

Pramit Choudhary explores the usefulness of a generative approach that applies Bayesian inference to generate human-interpretable decision sets in the form of "if. . .and else" statements. These human interpretable decision lists with high posterior probabilities might be the right way to balance between model interpretability, performance, and computation.
Operationalize deep learning: How to deploy and consume your LSTM networks for predictive maintenance scenarios

Operationalize deep learning: How to deploy and consume your LSTM networks for predictive maintenance scenarios

November 22, 2019

Francesca Lazzeri and Fidan Boylu Uz explain how to operationalize LSTM networks to predict the remaining useful life of aircraft engines. They use simulated aircraft sensor values to predict when an aircraft engine will fail in the future so that maintenance can be planned in advance.